Best AI Stocks to Buy in 2026 (Beyond NVIDIA)

NVIDIA hit a $3.5 trillion market cap in 2024 and became the most valuable company on Earth. Since then, it has consolidated and still trades at 30-40x forward earnings. The AI trade is no longer a secret — but it is far from over.

The difference in 2026 is that the money is moving down the stack. The hyperscalers (Microsoft, Google, Amazon) have already built their GPU clusters. Now the spending is shifting to:

• Custom silicon (Google's TPUs, Amazon's Trainium, Microsoft's Maia)
• Data center infrastructure (power, cooling, real estate)
• Enterprise AI software (agents, copilots, automation)
• AI utilities (the companies that actually make money from inference)

Here are the best AI stocks to buy in 2026, organized by where they sit in the AI value chain.

The AI Value Chain

Tier 1: The Chip Layer (Beyond NVIDIA)

NVIDIA still dominates AI training, but inference is where the real money is long-term — and inference is less dependent on NVIDIA's most expensive chips.

Broadcom (AVGO) — The Custom Silicon King

Broadcom designs custom AI chips for Google, Meta, and ByteDance. While NVIDIA sells GPUs off the shelf, Broadcom builds application-specific chips that are cheaper and more efficient at scale.

• Revenue from AI chips: $15B+ annually and growing 2x per year
• Dividend yield: ~1.3% with 14% annual dividend growth
• Valuation: More reasonable than NVIDIA at ~25x forward earnings

Why it matters: Every major tech company building custom silicon uses Broadcom's design expertise. This is a toll-road business on the AI highway.

AMD (AMD) — The Challenger

AMD's MI300 and MI350 chips are genuine alternatives to NVIDIA's H100/H200 for both training and inference. Microsoft, Meta, and Oracle have all placed large orders.

• MI300 revenue run rate exceeded $5B in 2025
• Server CPU market share continues to climb against Intel
• Valuation: ~20x forward earnings, cheaper than NVIDIA

The risk: AMD has a history of overpromising on GPU roadmaps. But the MI300 delivery was real, and MI350 looks competitive.

Marvell Technology (MRVL) — The Connective Tissue

Marvell makes the networking chips that connect AI clusters together. In a data center with 100,000 GPUs, the interconnect hardware is just as critical as the GPUs themselves.

• Custom silicon + optical networking for AI data centers
• Revenue growing 30%+ driven by AI demand
• Smaller company with more upside potential than Broadcom

Micron (MU) — Memory Matters

AI models need enormous amounts of high-bandwidth memory (HBM). Micron is one of three companies on Earth that makes HBM3E, alongside Samsung and SK Hynix.

• HBM revenue grew from $0 to $5B+ in two years
• Supply-constrained market means pricing power
• Historically cyclical, but AI demand may smooth the cycle

Tier 2: Cloud & Infrastructure

Constellation Energy (CEG) — Powering AI

This is the most underappreciated AI play of 2026. Data centers now consume 4% of US electricity, and AI workloads are doubling power demand every 2-3 years. There is not enough power.

Constellation operates nuclear power plants — the only carbon-free baseload power source that can reliably feed a 1-gigawatt data center. Microsoft signed a 20-year deal to restart Three Mile Island specifically for AI power.

• Nuclear restart strategy is unique in the market
• Long-term power purchase agreements provide revenue visibility
• ESG-friendly with zero-carbon credentials

Vertiv (VRT) — Cooling the Beast

AI chips run hot. A single NVIDIA GB200 rack consumes 120 kilowatts — equivalent to 100 homes. Traditional air cooling cannot handle this. Vertiv makes liquid cooling systems that are becoming mandatory.

• Data center cooling market growing 15%+ annually
• Liquid cooling attach rate rising from 10% to 50%+ of new builds
• Small-cap with genuine AI tailwind

Equinix (EQIX) and Digital Realty (DLR) — Data Center REITs

If you do not want to pick chip winners, own the real estate where AI lives. These REITs lease data center space to cloud providers and enterprises.

Tier 3: Enterprise Software

Salesforce (CRM) — AI Agents Go to Work

Salesforce's Agentforce platform lets companies deploy AI agents that actually perform tasks — schedule meetings, update records, handle customer service — rather than just generating text.

• Agentforce pricing: $2 per conversation, massive TAM
• Existing Salesforce customer base = instant distribution
• Trading at a discount to historical valuations

ServiceNow (NOW) — Enterprise Automation

ServiceNow is embedding AI across IT, HR, and customer service workflows. Their Now Assist product is seeing rapid adoption because it directly replaces human effort in ticket resolution.

• 25%+ revenue growth sustained for years
• AI products command premium pricing
• Recession-resistant — companies automate more in downturns

Palantir (PLTR) — Government AI

Palantir's AIP platform is becoming the default operating system for US defense AI, healthcare logistics, and supply chain optimization.

• US commercial revenue growing 50%+
• Government contracts provide deep moats
• Controversial, but the technology works

Tier 4: The ETF Approach

If picking individual AI stocks feels overwhelming, these ETFs offer diversified exposure:

For most investors, a combination of SMH (chips) + AIQ (broad AI) gives sufficient exposure without concentration risk.

Valuation Check: Is AI Too Expensive?

Surprisingly, NVIDIA is not expensive on a forward basis — if the growth continues. The risk is that AI capex growth slows and the multiple compresses.

The Money Printer Take

The AI boom of 2023-2024 was about building — buying GPUs, training models, proving the technology works. The AI boom of 2025-2026 is about deploying — actually making money from AI.

That shift favors different companies:

• 2023 winners: NVIDIA, AMD, pure-play chipmakers
• 2026 winners: Broadcom (custom silicon), Constellation (power), Salesforce (enterprise agents), data center REITs (real estate)

Do not chase last year's winners. The money in AI is rotating down the stack — from chips to power to software to actual utility.

Our play: 40% chip/core infrastructure, 30% power/data centers, 20% enterprise software, 10% speculative vertical AI. Rebalance quarterly.

FAQ

Is it too late to invest in AI? No. AI is where the internet was in 2000 — the technology works, but most companies have not figured out how to monetize it yet. The real returns may come from the "boring" infrastructure companies, not the flashy model builders.

Should I just buy NVIDIA and forget the rest? NVIDIA is a great company, but at $3+ trillion, the easy money has been made. Diversification into custom silicon (Broadcom), power (Constellation), and software (Salesforce) offers better risk-adjusted returns from here.

What about AI startups? Unless you are an accredited investor with access to venture funds, you cannot directly invest in AI startups. The public market alternatives above are your best option.

Are AI stocks in a bubble? Some are. Companies with no revenue and "AI" in their name are speculative. But the infrastructure companies — chipmakers, data centers, power utilities — have real earnings and real demand. Be selective.

How much of my portfolio should be in AI? AI is not a sector — it is a technology layer touching every sector. If you own a broad index fund like VTI or VOO, you already have 25-30% in tech/AI exposure. A 10-20% tactical overweight in pure AI plays is reasonable for most investors.